The invention relates to a method and an apparatus for segmentation of an object in a 2D or 3D image data set by extracting a path along the object in a selected region. Further, the invention relates to a computer program product.
Magnetic resonance angiography (MRA) images provide important information for the diagnosis of vascular disease, such as arterial stenosis and aneurysm. The recent development of MR blood-pool contrast agents which have extended intravascular half-life allows the acquisition of high resolution, high contrast 3D images of the vascular system.
The visualization of the vessel pathways is crucial to allow quick and reliable assessment of any potential problems. The most common visualization method is to construct a maximum intensity projection (MIP). However, with blood-pool contrast agents, the longer scan times necessary to achieve higher resolution require imaging during the steady state of contrast agent diffusion. Therefore, both arteries and veins are enhanced, and diagnostically important information (typically the arteries, where stenosis occurs) may be fully or partially occluded in the MIP.
Several approaches have been made for selecting voxels belonging to vessel regions. A wide spread approach for vessel enhancement is to use multi-scale orientation selective filters, based on eigen-analysis of the Hessian matrix, as e.g. known from “Model-based quantitation of 3D magnetic resonance angiographic images”, A. Frangi et al., IEEE Transactions on medical imaging, Vol. 18, No. 10, October 1999. Therein, linear vessel segments are modeled with a central vessel axis curve coupled to a vessel wall surface. The path is initialized using the shortest path across an image iso-surface. However, a significant limitation of filter-based approaches for vessel selection is that not all those voxels required to define a vessel structure fulfil the filter criteria, in particular those pixels near structural bifurcations. On the other hand, pixels not belonging to vessels may also be selected with filtering based approaches, for example in regions between two nearby vessel structures.
Also for the visualization of other objects, particularly anatomical structures like bones or airways in medical image data sets and also in 2D image data sets like images of the vessel tree, a method for segmentation of an object is often applied. It is thus an object of the present application to provide a method and an apparatus for accurately and automatically segmenting an object in an image data set.
This object is achieved by a method for segmentation comprising:    a) selecting a start point of the path as first active point,    b) adapting an adaptable model to the object in a first active section around the start point,    c) finding the next point of the selected region by the steps of:            c1) copying the geometric model parameters of the adapted model of the active point to a plurality of neighboring points of the active point,        c2) orienting a model around each of said plurality of neighboring points using said copied model parameters and searching the closest object points around each of said neighboring points based on said model,        c3) adapting the models to the found object points for each neighboring point,        c4) selecting the neighboring point for which the adapted model fits best to the object as next point of the selected region and as next active point, and            d) repeating step c) until an end point of the path or a predetermined number of iterations is reached.
According to one aspect, the user selects a start point as first action point in a first active region of the selected region in which the path shall be extracted. Preferably, also an end point is selected. Thus, the object of interest is identified which is then automatically segmented so that it can be suppressed from an image if required. For said segmentation prioritized region growing is used wherein iteratively voxels/pixels are added to a selected region based on a model adaptation and a selection for which point the model fits best to the object. The method according to the invention thus includes an iterative algorithm for finding the points of the path along the object during which the model, which ahs been adapted around a previous point of the path, is first copied to all neighboring points. Thereafter, for each neighboring point the models are adapted to the object by finding the closest object points. Based on the selection which model of which neighboring point fits best to the object the next point of the path from said neighboring points is selected.
During said prioritized region growing geometric model parameters, e.g. the radius, or cross-section parameters of the model, are propagated to neighboring points not yet having parameter estimates. In the first step, this means all the neighbors, but in subsequent steps, some of the neighbors will already have parameter estimates.
By the method, the boundary of the object can be accurately identified. Further, since particularly in vascular images venous and arterial pathways are often close together, it is possible, via the use of an appropriate geometric model, to discriminate between very closely separated structures, so that only anatomically connected pathways are selected. Further, the method is able to detect objects across a range of scales, which is important since the width of objects like vessels or airways can vary significantly.
Preferred embodiments are included in the dependent claims. The object is also achieved by an apparatus for segmentation comprising:    a) start point selection means for selecting a start point of the path as first active point,    b) first adaptation means for adapting an adaptable model to the object in a first active section around the start point,    c) path extracting means for finding the next point of the selected region by the steps of:            c1) copy means for copying the geometric model parameters of the adapted model of the active point to a plurality of neighboring points of the active point,        c2) search means for orienting a model around each of said plurality of neighboring points using said copied model parameters and for searching the closest object points around each of said neighboring points based on said model,        c3) second adaptation means for adapting the models to the found object points for each neighboring point,        c4) neighbor selection means for selecting the neighboring point for which the adapted model fits best to the object as next point of the selected region and as next active point, and            d) control means for repeating step c) until an end point of the path or a predetermined number of iterations is reached.
The application relates further to an apparatus for acquiring and processing medical image data, in particular a magnetic resonance apparatus, a computer tomography apparatus, an x-ray apparatus or an ultrasound apparatus, comprising means for acquiring medical image data and means for processing said image data including an apparatus for segmentation. Still further, the application relates to a computer program product comprising computer program means for causing a computer to perform the claimed steps when said computer program product is run on a computer. It shall be understood that these apparatuses and said computer program product can be developed further and can have similar embodiments as included in the claims.
For the selection which model fits best to the object, i.e. which of the neighboring points is the next point of the path, a distance measure is used based on the distances between the object and the model. Therefore, the distances along the normals from the surface of the model to the found object points are formed, and the distances along said normals are processed to form a distance measure for each model. This distance measure is combined with a gradient measure (sum of gradient across cylinder surface), and the model having the minimum combined measure is selected. The corresponding neighboring point will be selected as next point to be added to the selected region.
In order to find object points in the image data set that are closest to the models around the new neighboring points gradients of image values in the image data set are used. Particularly in medical images objects like vessels or airways show a high gradient at their borders compared to the gradient of the grey values of surrounding tissue.
In general, the present technique can also be used to segment a surface. Therefore, the surface is extracted by using planes along the surface as adaptable models and by adapting the planes to the surface.
A preferred application lies, as explained above, in the field of segmentation of objects in medical images. Preferably, tubular objects like vessels, bones and airways can be segmented in 3D medical image data sets. The start point is then selected within the tubular object, and the path within the object can be extracted, the path following mainly the centerline of the tubular object.
In general, any kind of three-dimensional model can be used when segmenting an object in a 3D image data set. When applying the present technique for segmentation of tubular objects, cylinder models have been shown useful the cross section of said cylinders being either circular, elliptical or even more close to the cross section of the tubular object to be segmented.
When using such cylinder models, in the first step of region growing, when parameters of the cylinder model are copied to all neighboring points of the previous point of the path, preferably the orientation and the radius of the cylinder model is copied to each neighboring point so that identical cylinder models are located around each neighboring point. After finding the closest object points to such cylinder models, the cylinder models are then adapted to the object by adapting the orientation and the radius of the cylinder models according to the detected object points.
According to the invention a cylinder model based boundary response is directly incorporated as a priority function during region growing leading to an increased accuracy of the segmentation. After completely extracting the path it is then possible to refine the models by combining all the models to create a single deformable model for the selected vessel segment, ant then to refine its surface, particularly by using a mesh on the surface and adapting the mesh to the object using a known method.
Preferably, the path is recovered from all selected points of the selected region by following said points in the order in which they were added to the selected region.